Production of Ethyl Esters by Direct Transesterification of Microalga Biomass Using Propane as Pressurized Fluid.

This work aimed to produce ethyl esters from Chlorella vulgaris microalgae biomass, using an immobilized enzymatic catalyst associated with pressurized fluid (propane) by direct transesterification. In order to optimize the ethyl conversion, different temperatures (46.7-68.1 °C) and pressures (59.5-200.5 bar) were applied a central composite design rotational (CCDR) obtaining the high conversion (74.39%) at 50 °C and 180 bar. The molar ratio also was investigated showing conversions ~ 90% using a molar ratio of 1:24 (oil:ethanol). From the best transesterification conditions, 50 °C, 180 bar, 20% enzymatic concentration, and 1:24 oil:ethanol molar ratio were obtained with success 98.9% conversion in 7 h of reaction. The enzyme reuse maintained its activity for three successive cycles. Thus, this simple process was effective to convert microalgal biomass into ethyl ester by direct transesterification and demonstrate high yields.

Lab-made electronic-nose with polyaniline sensor array used in classification of different aromas in gummy candies.

Aroma is closely related to the food product acceptability and an important product quality indicator. Electronic-nose (E-nose) systems are an interesting alternative to traditional methods of aroma analyses. A lab-made E-nose system equipped with an array of sensing units comprised by gold interdigitated microelectrodes (IDEs) using polyaniline (Pani) as sensitive layers deposited by the in situ and Layer-by-layer (LbL) methods was used to analyze aromas in gummy candies. Different concentrations from artificial aromas (apple, strawberry and grape), added to the gummy candies were evaluated. Our system presented 21.6 mV.ppb-1 sensitivity, ppb range detection limit, and good reversibility, around 97.6%. The sensitive layers of Pani films was adequate deposited on IDEs observed by the Attenuated Total Reflection/Fourier-transform infrared spectroscopy (ATR/FTIR). Linear Discriminant Analysis (LDA) was able to classify apple, strawberry, and grape aromas added to gummy candies using saturation potential values from the E-nose system, demonstrating its applicability in food matrices.